On the Create notebook instance page, provide the following
information:

For Notebook instance name, type
ExampleNotebookInstance.

For Instance type, choose
ml.t2.medium.

For IAM role, create an IAM role.

Choose Create a new role.

(Optional)
If you want to use S3 buckets other than the one
you created in Step 1 of this tutorial to store your input data and
output, choose them.

In
Step 1 of this tutorial, you created an S3 bucket
with sagemaker in its name.
This
IAM role automatically has permissions to use
that bucket. The AmazonSageMakerFullAccess policy,
which Amazon SageMaker attaches to the role, gives the role those permissions.

The bucket that you created in Step 1 is sufficient for the model
training exercise in
Getting
Started. However, as you explore Amazon SageMaker, you might
want to access other S3 buckets from your notebook instance. Give
Amazon SageMaker permissions to access those buckets.

If
your account has sensitive data (such as Human Resources
information), restrict access by choosing
Specific
S3 buckets.
You
can update the permissions policy attached to the role you
are creating later.

If you specified access to additional S3 bucket(s) when
creating theis role, the customer managed policy
attached to the role.
The
name of the customer managed policy is
AmazonSageMaker-ExecutionPolicy-YYYYMMDDTHHmmSS.

For Security Group, choose your VPCs default
security group. For the exercises in this guide, the inbound and
outbound rules of the default security group are sufficient.

To enable connecting to a resource in your VPC, ensure that the
resource resolves to a private IP address in your VPC. For example,
to ensure that an Amazon Redshift DNS name resolves to a private IP address, do
one of the following:

If you chose to access resources from your VPC, enable direct
internet access. For Direct internet access, choose
Enable.
Otherwise,
this notebook instance won't have internet access. Without internet access,
you can't train or host models from notebooks on this notebook instance
unless your VPC has a NAT gateway and your security group allows outbound
connections. For more information, see Notebook
Instances Are
Enabled
with Internet Access by
Default.

(Optional) If you want Amazon SageMaker to use an AWS Key Management Service key to
encrypt data in
the ML storage volume attached to the notebook instance, specify the key.

Choose Create notebook instance.

In a few minutes, Amazon SageMaker launches an ML compute instance—in this case, a
notebook instance—and attaches an ML storage volume to it. The
notebook instance has a preconfigured Jupyter notebook server and a set of
Anaconda libraries. For more information, see the CreateNotebookInstance API.

When the status of the notebook instance is InService, choose
Open next to its name to open the Jupyter dashboard.

The dashboard provides access to:

A folder that contains
sample
notebooks. To use a sample notebook, on the
Files tab, choose the
sample_notebook folder. For information about the
sample notebooks, see the Amazon SageMaker GitHub
repository.

The
kernels for Jupyter, including those that provide support for Python 2 and
3, Apache MXNet, TensorFlow, and PySpark. To choose a
kernel for your notebook
instance,
use the New menu.